An improved sea ice detection algorithm using MODIS: applicationas a new European sea ice extent indicator
Bibliographic record
Abstract
Abstract. The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat on biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved European sea ice extent indicator, the IceMap250 algorithm has been reworked to generate improved sea ice extent maps at 500 m resolution at nadir. Changes in the classification approach and a new method to correct artefacts arising from the MODIS cloud mask allow the enlargement of the mapped area, the reduction of potential error sources and a qualitative improvement of the resulting maps, while systematically achieving accuracies above 90 %. Monthly sea ice extent maps have been derived using a new synthesis method which acts as an additional error filter. Our results, covering the months of maximum (March) and minimum (September) sea ice extent during two decades (from 2000 to 2019), are a proof of the algorithm's applicability as an indicator, illustrating the sea ice decline in the European regional seas. We observed no significant trends in the Baltic (−2.75 ± 2.05 × 103 km2 yr−1) although, on the contrary, the European Arctic seas display clear negative trends both in March (−27.98 ± 6.01 × 103 km2 yr−1) and September (−16.47 ± 5.66 × 103 km2 yr−1). Such trends indicate that the sea ice cover in March and September is shrinking at a rate of ∼9 % and ∼13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼70 % greater in March. Therefore, according to the trends and without taking into account the variability of the sea ice cover, the loss of sea ice extent over two decades in the study area would be comparable to the area of continental France in the case of the March maximum, and to that of Finland in the case of the September minimum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".